A physics-informed 3D surrogate model for elastic fields in polycrystals Polycrystalline materials, which consist of numerous crystallites or grains, exhibit complex mechanical responses due to the interaction of their microstructural features. Accurately modeling the elastic fields in polycrystals is crucial for predicting material behavior under stress. Traditional methods, such as finite element analysis (FEA), are computationally expensive, particularly when dealing with three-dimensional (3D) microstructures. To address this challenge, physics-informed 3D surrogate models offer a promising alternative by integrating physical principles directly into machine learning frameworks. Physics-informed neural networks (PINNs) bridge the gap between data-driven approaches and traditional physics-based models. These models encode governing equations, such as the Navier-Cauchy equations for elasticity, as part of the loss function. By enforcing physical laws during training, PINNs...
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Precisely constructing asymmetric triple atoms for highly efficient electrocatalysis
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Precisely constructing asymmetric triple atoms for highly efficient electrocatalysis Triple-atom catalysts (TACs) are promising for surpassing the activity of normal single-atom and dual-atom catalysts. However, the rational design and construction of TACs remain challenging. Herein, we developed asymmetric Pt-Ru-Co triple atoms (TAs) by using selective atomic layer deposition technology. Compared with the corresponding single-atom and dual-atom counterparts, they demonstrate superior electrocatalytic performance in both the hydrogen evolution reaction (HER) and hydrogen oxidation reaction (HOR). Operando X-ray absorption spectroscopy (XAS) revealed that the heterogeneous atoms within Pt-Ru-Co TAs have strong interactions and serve as active centers, synergistically accelerating reaction kinetics. Additionally, theoretical calculations indicate that introducing Co atoms effectively optimizes the d orbital electronic structure of Pt and Ru, endowing e...
Outstanding Academic Achievement Award
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Outstanding Academic Achievement Award Academic achievement is a cornerstone of personal and societal development, marking a key aspect of an individual’s journey toward success and fulfillment. It is often viewed as a direct reflection of intellectual ability, hard work, and perseverance. Achieving excellence in academics, whether in elementary education or higher studies, sets the foundation for future career prospects and personal growth. The process of acquiring knowledge, developing critical thinking, and honing problem-solving skills is integral to shaping well-rounded individuals who contribute meaningfully to society. In an ever-evolving global landscape, academic success is increasingly important as it provides the necessary tools to adapt to technological advancements, global challenges, and dynamic job markets. Students who excel academically are often more adept at navigating complex situations, making informed decisions, and demonstrating leadership qualities in vari...
Evaluation of thermal scattering law and cross sections for liquid hydrogen fluoride☆
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Evaluation of thermal scattering law and cross sections for liquid hydrogen fluoride ☆ Liquid anhydrous hydrogen fluoride (HF) is a material commonly used in fuel manufacturing and processing, and as a result it is of particular interest for criticality safety applications. In order to capture the thermal scattering impacts from this hydrogenous material, accurate thermal scattering law (TSL, i.e. S(α,β)) libraries were developed. Using classical molecular dynamics (MD) simulation of the liquid HF system, a parametrized three-site model was developed in the GROMACS MD code to accurately represent the hydrogen bond and capture the liquid’s interatomic structure. This computational model (referenced as the NCSU HF model) was constructed with a massless charge to capture the hydrogen bonds between molecules. The accuracy of the NCSU HF model was verified by comparing its predictions of various HF properties with experimental data for the hydrogen and fluorine bond length...
Best Research Award
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Best Research Award Winning the Best Researcher Award is a prestigious honor that recognizes exceptional contributions to the world of academia and innovation. It is a testament to years of rigorous research, groundbreaking discoveries, and an unwavering passion for advancing knowledge. Researchers who achieve this distinction have demonstrated excellence in their field, influencing both current and future studies while driving meaningful change in society. Their work not only contributes to scientific progress but also addresses real-world challenges, offering solutions that impact industries, healthcare, technology, and beyond. This award serves as a motivation for aspiring researchers, encouraging them to push boundaries, embrace curiosity, and remain dedicated to their fields of study. It highlights the importance of persistence, creativity, and collaboration in the pursuit of new knowledge. Whether in medicine, engineering, environmental science, or any other dis...
Best Innovation Award
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Best Innovation Award Winning the Best Innovation Award is more than just an accolade; it is a symbol of excellence, perseverance, and forward-thinking. Innovators and organizations that receive this award have demonstrated their ability to think outside the box, tackle real-world challenges, and create solutions that drive meaningful change. These pioneers are the ones who introduce new technologies, improve efficiency, and make life better for people across the globe. Whether it’s a breakthrough in medical research, an advanced artificial intelligence system, a revolutionary clean energy solution, or a disruptive business model, each innovation recognized by this award serves as an inspiration for future generations. The importance of innovation cannot be overstated. In a rapidly evolving world, staying ahead of the curve requires continuous learning, adaptation, and the courage to challenge traditional methods. Companies that prioritize innovation gain a competitive edge, en...
Physics-Informed Neural Networks for the safety analysis of nuclear reactors
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Physics-Informed Neural Networks for the safety analysis of nuclear reactors This work explores the development of surrogate models for estimating the evolution of quantities of interest during nuclear reactor accident scenarios. Physics-Informed Neural Networks (PINNs) offer a promising surrogate modelling approach because they allow integrating laws of physics and domain knowledge into traditional Neural Network (NN) surrogates. Specifically, the proposed solution incorporates an additional term in the PINN loss function to enforce physics-based constraints in correspondence of allocation points, which are randomly sampled points whose corresponding target output is not known. As a result, accuracy of the estimation of the quantities of interest and their adherence to the laws of physics are improved. Applications to a synthetic case study and to the response of a nuclear microreactor system during a Loss of Heat Sink scenario confirm that the developed surrogate model bas...